the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Apr 2024
A novel framework for accurately quantifying wetland depression water storage capacity with coarse-resolution terrain data
Abstract. Accurate quantification of wetland depression water storage capacity (WDWSC) is imperative for comprehending the wetland hydrological regulation functions to support integrated water resources management. Considering the challenges posed by the high acquisition cost of high-resolution LiDAR DEM or the absence of field measurements for most wetland areas, urgent attention is required to develop an accurate estimation framework for WDWSC using open-source, low-cost, multi-source remote sensing data. In response, we developed a novel framework, WetlandSCB, utilizing coarse-resolution terrain data for accurate estimation of WDWSC. This framework overcame several technical difficulties, including biases in above-water topography, incompleteness and inaccuracy of wetland depression identification, and the absence of bathymetry. Validation and application of the framework were conducted in two national nature reserves of northeast China. The study demonstrated that integrating priority-flood algorithm, morphological operators and prior information can accurately delineate the wetland depression distribution with overall accuracy and Kappa coefficient both exceeding 0.95. The use of water occurrence map can effectively correct numerical biases in above-water topography with Pearson coefficient and R2 increasing by 0.33 and 0.38 respectively. Coupling spatial prediction and modeling with remote sensing techniques yielded highly accurate bathymetry estimates, with <3 % relative error compared to filed measurements. Overall, the WetlandSCB framework achieved estimation of WDWSC with <10 % relative error compared to field topographic and bathymetric measurements. The framework and its concept are transferable to other wetland areas globally where field measurements and/or high-resolution terrain data are unavailable, contributing to a major technical advancement in estimating WDWSC in river basins.
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RC1: 'Comment on hess-2024-71', Anonymous Referee #1, 20 Jun 2024
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This manuscript titled “A novel framework for accurately quantifying wetland depression water storage capacity with coarse-resolution terrain data” by Hu et al. presents a new framework towards better quantification of wetland depression water storage capacity, a critical component contributing to wetland hydrology. This proposed framework is capable to infer wetland water storage capacity with high accuracy and its statistical distribution from multisource information, including satellite data, historical maps, and prior knowledge. From my perspective, the research issue that this manuscript is targeting is interesting and requires urgent attention; moreover, the proposed framework can bring expertise to address this research question. Whereas I found a little difficult to follow in certain parts, where I think the authors need to further revise before the manuscript is ready to publish.
Firstly, I feel some disconnection between identified research questions and the proposed method. In line 207, the authors mention removing outliers from underwater area-level pairs. The concept of underwater area-level pairs seems to come to a sudden without much preparation. I’m confused about how do the authors obtain the underwater area-level pairs, from existing approaches like spatial prediction and remote sensing as reviewed in lines 88-117, or from some author-developed methods? If they come from existing approaches, wouldn’t it be a conflict since the authors just identified critical shortcomings to address in the review paragraph (lines 88-117)? Or if these area-level pairs are obtained from some approach developed by the authors, the method should be mentioned and details should be provided, either in the manuscript or supplemental material.
Another major comment is related to the discussion of the application of this proposed framework for integrated water resources management (Section 4.4). I agree that the wetland depressions are largely disregarded in hydrological models and that this proposed framework could bring possible improvements towards hydrological and water resources simulations, but by what means? The entire section is focused on the application and implication, but other than describing how it is important to account for wetland depression and the potential improvement it can bring, the authors do not depict a picture on how to fit the proposed framework into hydrological modeling and water resources management. To make the proposed framework more accessible to its potential clients (or to better sell the framework to hydrological modeling community), I suggest adding languages on how-to for the application part. If the authors have yet had a specific way for the application, it would also help by just providing general procedures. Additionally, a flowchart showing the application way will also be more than helpful.
Below are my minor comments:
[Line 75-76] The reviewer thinks it necessary to provide citations to support this claim.
[Line 207] A typo in “are-level pairs”.
Citation: https://doi.org/10.5194/hess-2024-71-RC1 -
AC1: 'Reply on RC1', Boting Hu, 03 Dec 2024
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Thank you very much for taking the time to review our manuscript and providing valuable and constructive comments. We greatly appreciate your insightful comments, which have been instrumental in helping us improve the quality of the manuscript. In response, we have diligently revised the manuscript according to your comments and suggestions.
Regarding the first comment: Firstly, I feel some disconnection between identified research questions and the proposed method. In line 207, the authors mention removing outliers from underwater area-level pairs. The concept of underwater area-level pairs seems to come to a sudden without much preparation. I’m confused about how do the authors obtain the underwater area-level pairs, from existing approaches like spatial prediction and remote sensing as reviewed in lines 88-117, or from some author-developed methods? If they come from existing approaches, wouldn’t it be a conflict since the authors just identified critical shortcomings to address in the review paragraph (lines 88-117)? Or if these area-level pairs are obtained from some approach developed by the authors, the method should be mentioned and details should be provided, either in the manuscript or supplemental material.
Response: Thank you for this valuable comment. We fully agree that the explanation of the underwater bathymetric estimation method was insufficient. Below, we provide our responses and revisions.
The absence of underwater bathymetric information in global DEMs is primarily due to the water distribution at the time of data acquisition. Taking SRTM DEM data as an example, the water extents in 2000 define the spatial extent of the water mask, which excludes land surface elevation measurements in the SRTM DEM data. With advancements in satellite radar/laser altimetry, data from Envisat, ICESat, CryoSat, Jason-1/2/3, SARAL, and Sentinel-3 have been available from 1992 to the present. Consequently, for each wetland, numerous water levels below the 2000 reference water level have been recorded. This enables the use of remote sensing technologies to construct underwater area-level pairs.
However, altimetry satellite data are subject to various factors that influence the accuracy of water level monitoring. These include intrinsic factors such as sensor performance and instrument resolution, as well as extrinsic factors like natural elements (e.g., clouds and wind), the geometry of the wetland water body, boundary conditions, and vegetation characteristics (Donlon et al., 2012; Gao et al., 2019; Zhou et al., 2023). Consequently, the derived water level data exhibit substantial variability and uncertainty. For instance, Figure 1 presents the water level variations extracted from two altimetry satellites, Sentinel-3 and ICESat-2, for the Huaao wetland in the Nenjiang River Basin, where water level differences during the same period can reach up to 2 meters. Relying solely on mathematical methods (e.g., the coefficient of variation) or on natural rules (e.g., water levels in wet seasons being higher than those in dry seasons) proposed in previous studies is insufficient for accurately determining water level values.
To address the challenge, this study proposes an improved method for estimating underwater topography in wetland depressions by integrating spatial prediction and remote sensing techniques. The method is based on the assumption that the relationship between water surface area and water level is unique and that the slope of the above-water topography is generally continuous with that of the underwater topography. By systematically iterating through combinations of water surface area and water level, we identify the combination that best matches the slope of the above-water topography, as corrected by the WetlandSCB framework, as the optimal solution for characterizing the underwater topography of wetland depressions.
Figure 1. (a) Huaao wetland depression location. (b) water level data extraction from Sentinel-3 and ICESat-2 altimetry satellites.
To address the discontinuity of the manuscript, I have added the following content before line 207: "Match multi-source altimetry satellites with optical images to construct all area–level pairs for wetland depressions. By identifying water surface distributions in global DEMs, filter the area–level pairs that represent underwater hypsometric relationships within wetland depressions."
References:
Donlon, C., Berruti, B., Buongiorno, A., Ferreira, M.H., F´em´enias, P., Frerick, J., Goryl, P., and Sciarra, R.: The global monitoring for environment and security (GMES) sentinel-3 mission. Remote Sens. Environ. 120, 37–57. https://doi.org/10.1016/j.rse.2011.07.024, 2012.
Gao, Q., Makhoul, E., Escorihuela, M.J., Zribi, M., Quintana Seguí, P., García, P., and Roca, M.: Analysis of retrackers’ performances and water level retrieval over the Ebro River basin using sentinel-3. Remote Sens. 11 (6), 718. https://doi.org/ 10.3390/rs11060718, 2019.
Zhou, H., Liu, S., Mo, X., Hu, S., Zhang, L., Ma, J., Bandini, F., Grosen, H., and BauerGottwein, P.: Calibrating a hydrodynamic model using water surface elevation determined from ICESat-2 derived cross-section and Sentinel-2 retrieved sub-pixel river width. Remote Sens. Environ. 298, 113796. https://doi.org/10.1016/j. rse.2023.113796, 2023.
Regarding the second comment: Another major comment is related to the discussion of the application of this proposed framework for integrated water resources management (Section 4.4). I agree that the wetland depressions are largely disregarded in hydrological models and that this proposed framework could bring possible improvements towards hydrological and water resources simulations, but by what means? The entire section is focused on the application and implication, but other than describing how it is important to account for wetland depression and the potential improvement it can bring, the authors do not depict a picture on how to fit the proposed framework into hydrological modeling and water resources management. To make the proposed framework more accessible to its potential clients (or to better sell the framework to hydrological modeling community), I suggest adding languages on how-to for the application part. If the authors have yet had a specific way for the application, it would also help by just providing general procedures. Additionally, a flowchart showing the application way will also be more than helpful.
Response: Thank you for this valuable suggestion. Over the past three months, I have given it considerable thought and taken actions to address it. The VIC-5 model incorporates a specialized lake/wetland module, and I have precisely prepared input parameters based on the WetlandSCB framework, such as wetland hypsometric relationships, wetland catchment area, and other module-relevant parameters. Furthermore, I have refined the parameters of wetland soil and vegetation parameters (e.g., wetland soil depth is approximately 1 meter, and the dominant vegetation is reeds in the Nenjiang River Basin). Using these inputs, I conducted simulations to evaluate the impacts of wetland dynamics on soil moisture and runoff. The results exhibited a high degree of accuracy, highlighting the strong potential of the WetlandSCB framework to enhance wetland eco-hydrological modeling studies.
Figure 2. Accuracy assessment of the VIC hydrological model with lake/wetland module.
To provide guidance on "how-to for the application", I have added the following content and a flowchart in Section 4.4: "Using the WetlandSCB framework, raster-scale wetland depression topographic information can be accurately reconstructed. Through flow direction analysis and watershed delineation methods, key parameters such as wetland inflow and outflow locations, wetland catchment boundaries, and other related characteristics can be identified (these steps can be performed using QGIS software). By integrating the hypsometric curve, water surface distribution data, and morphological characteristics of the wetland derived from the WetlandSCB framework, the initial wetland water level, the number of wetland layers, and the corresponding area–level pairs can be determined. Field surveys provide essential data on wetland soil and vegetation properties as well as inflow volumes within the study area. Finally, the hydrological model, coupled with the wetland module, can be implemented to support wetland eco-hydrological research and integrated water resources management."
Figure 3. Integration process and application outputs of the WetlandSCB framework with a hydrological model.
In terms of minor comments:
[Line 75-76] The reviewer thinks it necessary to provide citations to support this claim.
Response: Thank you for this comment. We have provided the following references.
Chen, T., Song, C., Zhan, P., Yao, J., Li, Y., and Zhu, J.: Remote sensing estimation of the flood storage capacity of basin-scale lakes and reservoirs at high spatial and temporal resolutions, Sci.Total Environ, 807, 150772, https://doi.org/10.1016/j.scitotenv.2021.150772, 2022.
Liu, K., Song, C., Zhao, S., Wang, J., Chen, T., Zhan, P., Fan, C., and Zhu, J.: Mapping inundated bathymetry for estimating lake water storage changesfrom SRTM DEM: A global investigation. Remote Sens. Environ, 301, 113960, https://doi.org/10.1016/j.rse.2023.113960, 2024.
Liu, K., Song, C., Ke, L., Jiang, L., Pan, Y., and Ma, R.: Global open-access DEM performances in Earth's most rugged region High Mountain Asia: Amulti-level assessment. Geomorphology, 338, 16-26, https://doi.org/10.1016/j.geomorph.2019.04.012, 2019.
[Line 207] A typo in “are-level pairs”.
Response: Thank you for this comment. We have revised it to “area-level pairs”.
Citation: https://doi.org/10.5194/hess-2024-71-AC1
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AC1: 'Reply on RC1', Boting Hu, 03 Dec 2024
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